mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 04:08:09 +03:00
187 lines
10 KiB
Plaintext
187 lines
10 KiB
Plaintext
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include _includes/_mixins
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- var logos = [ ['chartbeat', 'https://chartbeat.com'], ['socrata', 'https://www.socrata.com'], ['keyreply', 'https://keyreply.com/'], [ 'kip', 'http://kipthis.com'], ['cytora', 'http://www.cytora.com'], ['signaln', 'http://signaln.com'] ]
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//- Landing Page
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//- ============================================================================
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header.header
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.header-body
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+h1.header-title.header-text
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| Industrial-strength#[br]
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| Natural Language#[br]
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| Processing
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+lead.header-text
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strong
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| Thousands of researchers are trying to make#[br]
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| computers understand text. They're succeeding.#[br]
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| We help you get their work out of papers and#[br]
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| into production.#[br]
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+button('secondary')(href='/docs/#install') Install spaCy
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+divider('bar')
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| #[a(href='https://github.com/' + profiles.github + '/spaCy/releases' target='_blank'): #[strong Latest Release:] v#{spacy_version}]
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| #[a(href='https://github.com/' + profiles.github + '/spacy' target='_blank'): +icon('github', 'secondary') #[strong #{spacy_stars}+ stars] on GitHub]
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| #[a(href='https://www.reddit.com/r/' + profiles.reddit target='_blank'): +icon('reddit', 'secondary') #[strong User Group] on Reddit]
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main.main
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+grid('padding', 'space-between')
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+grid-col('half')
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+h2 Built for Production
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p.text-big.
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Most AI software is built for research. Over the last ten years,
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we've used a lot of that software, and built some of it ourselves,
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especially for natural language processing (NLP).
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But the faster the research has moved, the more impatient we've
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become. We want to see advanced NLP technologies get out into
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great products, as the basis of great businesses. We built spaCy
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to make that happen.
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+h2 Easy and Powerful
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p.text-big.
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For any NLP task, there are always lots of competing algorithms.
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We don't believe in implementing them all and letting you choose.
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Instead, we just implement one – the best one.
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When better algorithms are developed, we can update the library without
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breaking your code or bloating the API. This approach makes spaCy
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both #[strong easier] and #[strong more powerful] than a pluggable
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architecture. spaCy also features a #[strong unique whole-document design].
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Where other NLP libraries rely on sentence detection as
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a pre-process, spaCy reads the whole document at once, making it much more
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robust to informal and poorly formatted text.
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+h2 Permissive open-source license (MIT)
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p.text-big.
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We think spaCy is valuable software, so we made it free, to raise
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its value even higher.
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Making spaCy open-source puts us on the same side –
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we can tell you everything about how it works, and let
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you run it however you like. We think the software would be much
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less valuable as a service, which could disappear at any point.
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+grid-col('half', 'valign-bottom')
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+code-demo('lightning_tour.py').block.
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# pip install spacy && python -m spacy.en.download
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import spacy
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# Load English tokenizer, tagger, parser, NER and word vectors
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nlp = spacy.load('en')
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# Process a document, of any size
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text = open('war_and_peace.txt').read()
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doc = nlp(text)
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from spacy.attrs import *
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# All strings mapped to integers, for easy export to numpy
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np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
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from reddit_corpus import RedditComments
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reddit = RedditComments('/path/to/reddit/corpus')
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# Parse a stream of documents, with multi-threading (no GIL!)
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# Processes over 100,000 tokens per second.
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for doc in nlp.pipe(reddit.texts, batch_size=10000, n_threads=4):
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# Multi-word expressions, such as names, dates etc
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# can be merged into single tokens
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for ent in doc.ents:
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ent.merge(ent.root.tag_, ent.text, ent.ent_type_)
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# Efficient, lossless serialization --- all annotations
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# saved, same size as uncompressed text
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byte_string = doc.to_bytes()
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+h2.text-center
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+label('strong') spaCy is trusted by
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+grid('space-around', 'valign-center', 'padding')
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each logo in logos
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a(href=logo[1] target='_blank')
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img(src='assets/img/logos/' + logo[0] + '.png').logo--small
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+divider
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+grid
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+grid-col('half', 'valign-center', 'align-center')
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.image-container
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img(src='assets/img/spacy_screen.png' style='display: block')
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+grid-col('half')
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+h2
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+label('strong') About spaCy
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.h2 What we do
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p.text-big spaCy helps you write programs that do clever things with text. You give it a string of characters, it gives you an object that provides multiple useful views of its meaning and linguistic structure. Specifically, spaCy features a high performance tokenizer, part-of-speech tagger, named entity recognizer and syntactic dependency parser, with built-in support for word vectors. All of the functionality is united behind a clean high-level Python API, that makes it easy to use the different annotations together.
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p.text-big To make spaCy as fast and easy to install as we could, we built it #[strong from the ground up] from custom components, with #[strong custom implementations], and sometimes #[strong custom algorithms]. It's written in clean but efficient Cython code, which allows us to manage both low level details and the high-level Python API in a single codebase.
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+divider
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+h2.text-center
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+label('strong') What our users say...
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+grid('padding')
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+grid-col('third', 'valign-center')
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<blockquote class="twitter-tweet" data-cards="hidden" data-lang="en"><p lang="en" dir="ltr">"Dead Code Should be Buried" <a href="http://t.co/AxfZRRz8nB">http://t.co/AxfZRRz8nB</a> by <a href="https://twitter.com/honnibal">@honnibal</a> on NLP tools & new Python library spaCy <a href="http://t.co/C9f798R3aO">http://t.co/C9f798R3aO</a> looks nice!</p>— Andrej Karpathy (@karpathy) <a href="https://twitter.com/karpathy/status/640098689894232064">September 5, 2015</a></blockquote>
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+grid-col('third', 'valign-center')
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<blockquote class="twitter-tweet" data-cards="hidden" data-lang="en"><p lang="en" dir="ltr">spaCy seems pretty exciting to me - and it is clear that NLTK has not kept up with <a href="https://twitter.com/hashtag/NLP?src=hash">#NLP</a>. <a href="http://t.co/mUPFUMLrbo">http://t.co/mUPFUMLrbo</a> <a href="https://twitter.com/hashtag/python?src=hash">#python</a> <a href="https://twitter.com/hashtag/datascience?src=hash">#datascience</a></p>— Alex Engler (@AlexCEngler) <a href="https://twitter.com/AlexCEngler/status/648537133544833025">September 28, 2015</a></blockquote>
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+grid-col('third', 'valign-center')
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<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Explore what NLP can do these days with Sense2vec and Spacy <a href="https://t.co/MHZEP2yLo4">https://t.co/MHZEP2yLo4</a></p>— Chattermill.io (@chatter_mill) <a href="https://twitter.com/chatter_mill/status/699660272907059200">February 16, 2016</a></blockquote>
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+divider
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+grid('padding', 'valign-center')
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+grid-col('half')
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+h2
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.label-strong Benchmarks
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.h2 State-of-the-art speed and accuracy
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p.text-big spaCy is committed to rigorous evaluation under standard methodology. Two peer-reviewed papers in 2015 confirm that it offers the #[strong fastest syntactic parser in the world] and that #[strong its accuracy is within 1% of the best] available. The few systems that are more accurate are 20× slower or more.
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p.text-big The first of the evaluations was published by #[strong Yahoo! Labs] and #[strong Emory University], as part of a survey of current parsing technologies #[a(href="http://aclweb.org/anthology/P/P15/P15-1038.pdf" target="_blank") (Choi et al., 2015)]. Their results and subsequent discussions helped us develop a novel psychologically-motivated technique to improve spaCy's accuracy, which we published in joint work with Macquarie University #[a(href="https://aclweb.org/anthology/D/D15/D15-1162.pdf" target="_blank") (Honnibal and Johnson, 2015)].
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+grid-col('half')
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+table(["System", "Language", "Accuracy", "Speed (WPS)"])
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+row
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+cell #[+logo('tiny')]
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+cell #[strong Cython]
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+cell #[strong 91.8]
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+cell #[strong 13,963]
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+row
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+cell ClearNLP
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+cell Java
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+cell 91.7
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+cell 10,271
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+row
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+cell CoreNLP
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+cell Java
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+cell 89.6
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+cell 8,602
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+row
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+cell MATE
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+cell Java
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+cell 92.5
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+cell 550
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+row
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+cell Turbo
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+cell C++
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+cell 92.4
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+cell 349
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+divider
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+h2.text-center
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.label-strong Latest Blog Posts
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.h3 Read more about NLP
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!=partial('_includes/_latest-posts', { max: 3 } )
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!=partial('_includes/_newsletter', { divider: 'top' } )
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script(async src="https://platform.twitter.com/widgets.js" charset="utf-8")
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